Deep‐Learning Classification of Sub‐Seismic‐Scale Lithology Using Angle‐Dependent Time–Frequency Attributes

ABSTRACT

Traditional seismic methods struggle to characterize sub‐seismic scale (i.e., smaller than one‐tenth of the wavelength) geological structures, limiting accurate determination of their thickness, lithology and layering. To improve characterization, many studies have applied amplitude‐versus‐angle and spectral decomposition analyses using seismic reflectivity images. However, the use of spectral decomposition for angle‐dependent images remains largely unexplored, suggesting the potential for enhanced subsurface analysis. We hypothesize that seismic attributes extracted from angle‐dependent images can provide additional information for identifying geological features associated with sub‐seismic scale structures. To investigate this, we construct various geological models, ranging from simple horizontal layers to more realistic configurations. Using a pre‐stack depth migration approach, we obtain angle‐dependent images, followed by spectral decomposition to derive seismic attributes. These attributes are then used to train a deep neural network (DNN) (ResNet‐50) to assess their effectiveness in identifying complex geological features. Our results show that for layers at the sub‐seismic scale, geological features that are not easily distinguishable in conventional reflectivity images become identifiable when both angle‐dependent and scale‐dependent seismic attributes are introduced, where the DNN demonstrates significantly improved performance. Based on these findings, we propose a multi‐attribute deep learning approach trained on angle‐dependent spectral scalograms from complementary angle ranges. On synthetic data, this approach achieves an F1‐score of 96.59%, an improvement of about 16 percentage points over a conventional reflectivity baseline. We further validate it on the publicly available Penobscot three‐dimensional field dataset using well‐anchored proxy labels and a spatially conservative train/test split, where the SC‐ZNM (scalogram on zero‐, near‐ and mid‐angle ranges) configuration again achieves the highest balanced accuracy and macro F1‐score among all evaluated representations. These results indicate that incorporating angle‐dependent spectral representations into multi‐attribute deep learning enhances the characterization of sub‐seismic scale structures, with demonstrated applicability to real field data. The scope of this study is the classification of sub‐seismic‐scale lithology and layering; estimation of continuous reservoir properties such as porosity and water saturation is a distinct regression problem that lies beyond the present work and is identified as a direction for future research.